Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zgbrenner/agentcounsel --skill sanctions-screening-reviewgit clone --depth 1 https://github.com/zgbrenner/agentcounselWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zgbrenner/agentcounsel/sanctions-screening-review)<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/sanctions-screening-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/sanctions-screening-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zgbrenner/agentcounsel/sanctions-screening-review"><img src="https://agentmods.dev/badge/skills/zgbrenner/agentcounsel/sanctions-screening-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00050 | $0.02116 |
| Opus 5 | $0.00025 | $0.01058 |
| Sonnet 5 | $0.00010 | $0.00423 |
| Haiku 4.5 | $0.00005 | $0.00212 |
Grade A, and why
Sanctions Screening Review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sanctions Screening Review
Purpose
Produce a structured, review-ready draft adjudication of sanctions, politically exposed person (PEP), and adverse-media screening results. The skill compares the screened party's identifiers against each matched list entry, classifies every potential match by confidence, separates likely false positives from genuine hits, and proposes a disposition for each alert.
This skill provides workflow discipline and analytical structure. It produces draft work product for review by the firm's compliance function and a supervising attorney. This is not legal advice and not an alert-clearing decision. A potential match is not a confirmed match, and a confirmed match is not by itself a finding of wrongdoing.
Use When
- A user says "review these screening hits," "adjudicate these PEP matches," or "help me work through these sanctions alerts."
- A screening run — at onboarding or as part of ongoing monitoring — has generated potential matches that need first-pass review.
- A firm needs a structured comparison and confidence classification before a compliance officer dispositions alerts.
Required Inputs
- The screening results: the actual alert list or hit report — the screened name, the list source for each hit, the matched list entry, and the match score where one is given. If no screening results are provided, stop and request them.
- Identifying data for the screened party: date of birth or formation date, nationality or jurisdiction, addresses, and any identifiers — so the screened party can be compared against the matched entry.
- The firm's screening or alert-disposition policy: the firm document setting out match thresholds, false-positive criteria, and escalation rules. If not provided, stop and request it. Do not apply thresholds from model background knowledge.
- Screening context: the lists screened against, the as-of date of the screening run, and whether this is onboarding or ongoing monitoring.
If the screening results or the disposition policy is missing, stop and request it.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 137 lines · 50 tokens per session scan A 39da8e1a6dab
Sanctions Screening Review is a skill published in the GitHub repository zgbrenner/agentcounsel (19 stars, last pushed 1mo ago), licensed MIT. It adds 50 tokens to every session and 2,116 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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